# coding=utf-8
# Copyright 2022 The HuggingFace Team and Microsoft Research AI4Science All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
""" BioGPT model configuration"""

from mindnlp.utils import logging
from ...configuration_utils import PretrainedConfig


logger = logging.get_logger(__name__)

BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "microsoft/biogpt": "https://hf-mirror.com/microsoft/biogpt/resolve/main/config.json",
    # See all BioGPT models at https://hf-mirror.com/models?filter=biogpt
}


class BioGptConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`BioGptModel`]. It is used to instantiate an
    BioGPT model according to the specified arguments, defining the model architecture. Instantiating a configuration
    with the defaults will yield a similar configuration to that of the BioGPT
    [microsoft/biogpt](https://hf-mirror.com/microsoft/biogpt) architecture.

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.


    Args:
        vocab_size (`int`, *optional*, defaults to 42384):
            Vocabulary size of the BioGPT model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`BioGptModel`].
        hidden_size (`int`, *optional*, defaults to 1024):
            Dimension of the encoder layers and the pooler layer.
        num_hidden_layers (`int`, *optional*, defaults to 24):
            Number of hidden layers in the Transformer encoder.
        num_attention_heads (`int`, *optional*, defaults to 16):
            Number of attention heads for each attention layer in the Transformer encoder.
        intermediate_size (`int`, *optional*, defaults to 4096):
            Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
        hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
            `"relu"`, `"selu"` and `"gelu_new"` are supported.
        hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
            The dropout ratio for the attention probabilities.
        max_position_embeddings (`int`, *optional*, defaults to 1024):
            The maximum sequence length that this model might ever be used with. Typically set this to something large
            just in case (e.g., 512 or 1024 or 2048).
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        layer_norm_eps (`float`, *optional*, defaults to 1e-12):
            The epsilon used by the layer normalization layers.
        scale_embedding (`bool`, *optional*, defaults to `True`):
            Scale embeddings by diving by sqrt(d_model).
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models). Only
            relevant if `config.is_decoder=True`.
        layerdrop (`float`, *optional*, defaults to 0.0):
            Please refer to the paper about LayerDrop: https://arxiv.org/abs/1909.11556 for further details
        activation_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for activations inside the fully connected layer.
        pad_token_id (`int`, *optional*, defaults to 1):
            Padding token id.
        bos_token_id (`int`, *optional*, defaults to 0):
            Beginning of stream token id.
        eos_token_id (`int`, *optional*, defaults to 2):
            End of stream token id.

    Example:
        ```python
        >>> from transformers import BioGptModel, BioGptConfig
        ...
        >>> # Initializing a BioGPT microsoft/biogpt style configuration
        >>> configuration = BioGptConfig()
        ...
        >>> # Initializing a model from the microsoft/biogpt style configuration
        >>> model = BioGptModel(configuration)
        ...
        >>> # Accessing the model configuration
        >>> configuration = model.config
        ```
    """
    model_type = "biogpt"

    def __init__(
        self,
        vocab_size=42384,
        hidden_size=1024,
        num_hidden_layers=24,
        num_attention_heads=16,
        intermediate_size=4096,
        hidden_act="gelu",
        hidden_dropout_prob=0.1,
        attention_probs_dropout_prob=0.1,
        max_position_embeddings=1024,
        initializer_range=0.02,
        layer_norm_eps=1e-12,
        scale_embedding=True,
        use_cache=True,
        layerdrop=0.0,
        activation_dropout=0.0,
        pad_token_id=1,
        bos_token_id=0,
        eos_token_id=2,
        **kwargs,
    ):
        """
        Initializes a new instance of the BioGptConfig class.
        
        Args:
            self: The instance of the class.
            vocab_size (int): The size of the vocabulary. Defaults to 42384.
            hidden_size (int): The size of the hidden layers. Defaults to 1024.
            num_hidden_layers (int): The number of hidden layers. Defaults to 24.
            num_attention_heads (int): The number of attention heads. Defaults to 16.
            intermediate_size (int): The size of the intermediate layers. Defaults to 4096.
            hidden_act (str): The activation function for the hidden layers. Defaults to 'gelu'.
            hidden_dropout_prob (float): The dropout probability for the hidden layers. Defaults to 0.1.
            attention_probs_dropout_prob (float): The dropout probability for the attention probabilities. Defaults to 0.1.
            max_position_embeddings (int): The maximum number of position embeddings. Defaults to 1024.
            initializer_range (float): The range for the initializer. Defaults to 0.02.
            layer_norm_eps (float): The epsilon value for layer normalization. Defaults to 1e-12.
            scale_embedding (bool): Whether to scale the embedding. Defaults to True.
            use_cache (bool): Whether to use caching. Defaults to True.
            layerdrop (float): The probability of dropping a layer. Defaults to 0.0.
            activation_dropout (float): The dropout probability for the activation. Defaults to 0.0.
            pad_token_id (int): The id of the padding token. Defaults to 1.
            bos_token_id (int): The id of the beginning-of-sentence token. Defaults to 0.
            eos_token_id (int): The id of the end-of-sentence token. Defaults to 2.
        
        Returns:
            None.
        
        Raises:
            None.
        """
        self.vocab_size = vocab_size
        self.max_position_embeddings = max_position_embeddings
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.intermediate_size = intermediate_size
        self.hidden_act = hidden_act
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.initializer_range = initializer_range
        self.layer_norm_eps = layer_norm_eps
        self.scale_embedding = scale_embedding
        self.use_cache = use_cache
        self.layerdrop = layerdrop
        self.activation_dropout = activation_dropout
        super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)

__all__ = ['BioGptConfig']
